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Record W2982384806 · doi:10.1021/acscatal.9b04023

Photochemical CO<sub>2</sub> Reduction Driven by Water-Soluble Copper(I) Photosensitizer with the Catalysis Accelerated by Multi-Electron Chargeable Cobalt Porphyrin

2019· article· en· W2982384806 on OpenAlexafffund
Xian Zhang, Mihaela Cibian, Arnau Call, Kosei Yamauchi, Ken Sakai

Bibliographic record

VenueACS Catalysis · 2019
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersFonds de recherche du Québec – Nature et technologiesJapan Society for the Promotion of ScienceChina Scholarship CouncilMinistry of Education, Culture, Sports, Science and Technology
KeywordsPhotosensitizerPorphyrinCobaltPhotochemistryCatalysisChemistryElectron transferAqueous solutionPhotocatalysisArtificial photosynthesisElectron acceptorElectron donorIntramolecular forceSelectivityInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Without using precious elements, a highly efficient and selective molecular-based photocatalytic system for CO 2 -to-CO conversion in fully aqueous media has been developed. Our copper(I)-based water-soluble photosensitizer ( CuPS ) preserves its highly luminescent and long-lived excited state even in aqueous media. The CuPS -driven CO 2 reduction catalyzed by a water-soluble cobalt porphyrin possessing four N -methylpyridinium acceptors at the meso positions ( CoTMPyP ) achieves the highest catalytic activity among those reported for aqueous systems: TON CO = 2680 and TOF CO max = 1600–2600 h –1 with Sel CO2 = 77–90% (selectivity for CO vs H 2 ). The observed photocatalytic enhancement is discussed in terms of the 6-electron chargeable character of CoTMPyP, permitting its rapid release of CO via reduction of Co II to Co I by intramolecular electron transfer from the reducing equivalent stored at one of the acceptors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.222
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations128
Published2019
Admission routes2
Has abstractyes

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